Identification of traffic control mechanisms using machine learning

ABSTRACT

A device can receive a data model that has been trained on base map data and summary statistics data associated with a first geographic region. The device can obtain additional base map data associated with a second geographic region and additional summary statistics data for a set of junctions within the second geographic region. The device can determine traffic control mechanisms associated with the set of junctions by providing the additional base map data and the additional summary statistics data as input for the data model. The device can generate, using output of the data model, a base map that includes information indicating whether the set of junctions include traffic control mechanisms. The device can, after generating the base map, perform one or more actions associated with improving vehicle navigation or traffic management.

BACKGROUND

A navigation system can utilize geographic map data to identify a pathfor a user. For example, a navigation system can receive arrivalcoordinates and destination coordinates, and can use pathing algorithmsand geographic map data (e.g., road-side speed limits, a number of lanesassociated with particular roads, etc.) to identify a path for a user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D are diagrams of an overview of an example implementationdescribed herein;

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, can be implemented;

FIG. 3 is a diagram of example components of one or more devices of FIG.2; and

FIG. 4 is a flow chart of an example process for performing one or moreactions associated with improving vehicle navigation and/or trafficmanagement by generating and using a base map that includes informationidentifying traffic control mechanisms at one or more junctions in ageographic region.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings can identify the same or similar elements.

Geographic map data (referred to hereafter as base map data) can be usedto make vehicle navigation decisions and/or traffic managementdecisions. For example, a user device can provide a request for a set ofnavigational directions that includes an arrival location and adestination location. In this case, the device can use the base map dataand one or more path-finding algorithms to identify a path for a user.However, limited information included in the base map data can lead toexecution of inefficient or unsafe path-finding algorithms. For example,base map data that does not identify traffic control mechanisms (e.g.,traffic lights, stop signs, etc.), or that sporadically identifiestraffic control mechanisms, can cause path-finding algorithms to outputinefficient or unsafe paths. Additionally, base map data withoutproperly identified traffic control mechanisms can lead to unclear voicemessages for users that receive navigational directions, incorrectestimated time of arrival (ETA) predictions, and/or the like.

Some implementations described herein provide a location managementplatform to use machine learning to generate a base map that identifiestraffic control mechanisms at a set of junctions located within ageographic region, where the base map can then be utilized to performvehicle navigation decisions and/or traffic management decisions. Forexample, the location management platform can obtain base map data andobservation data, and can analyze the observation data to determine pathdata and summary statistics data. In this case, the location managementplatform can use the base map data, the path data, and the summarystatistics data to train a data model.

Additionally, the location management platform can use the data model toidentify traffic control mechanisms at a set of junctions (e.g.,intersections) located within a geographic region. For example, thelocation management platform can provide additional base map data,additional path data, and additional summary statistics data as inputfor the data model, which can cause the data model to output, for eachjunction of the set of junctions, a classification identifying a trafficcontrol mechanism (or lack thereof) used for a particular direction ofeach junction. Furthermore, the location management platform can use theoutput of the data model to generate a base map that identifies thetraffic control mechanisms, and can use the base map to perform actionsassociated with improving vehicle navigation and/or traffic management.

For example, the location management platform can use a base map toidentify a best-fit path for a user, provide clearer voice messagesassociated with navigational directions, provide more accuratedestination arrival time predictions, generate recommendations toinstall, remove, or make a modification to a traffic control mechanismat a particular junction, and/or the like. In this way, the locationmanagement platform conserves processing resources and/or networkresources relative to devices that are unable to utilize a base map thatidentifies traffic control mechanisms.

FIGS. 1A-1D are diagrams of an overview of an example implementation 100described herein. As shown in FIG. 1A, and by reference number 105, alocation management platform can obtain, from a base map providerdevice, base map data for a first geographic region (e.g., a city). Forexample, the location management platform can obtain base map data thatis open-source, base map data that is commercially available, and/or thelike. The base map data can include a set of values indicatingattributes of a road map, such as geographic coordinates of junctions,geographic coordinates of areas between the junctions, functional roadclasses, speed limits, a number of lanes associated with a road, and/orthe like.

As shown by reference number 110, the location management platform canobtain, from a set of location aware devices, observation data for a setof vehicles within the first geographic region. A location aware devicecan be a mobile device (e.g., a mobile phone, a tablet, a hand-heldnavigation device, etc.), an in-dash device located within a vehicle,and/or the like. The observation data can include a set of values usedto identify a location of a vehicle at a particular time period, such asa geographic location of the vehicle, a time stamp, a vehicleidentifier, a vehicle speed, and/or the like.

As shown by reference number 115, the location management platform candetermine path data and summary statistics data. For example, thelocation management platform can determine path data by analyzing theobservation data throughout an interval. The path data can identify apath that a vehicle travels, and can be determined by creating logicalconnections between sets of geographic coordinates included in theobservation data. In some cases, the location aware devices might notreport vehicle speed and/or vehicle direction. In this case, thelocation management platform can determine vehicle speed and/or vehicledirection using the geographic coordinates and time stamps included inthe observation data.

Additionally, the location management platform can determine summarystatistics data. For example, the location management platform candetermine summary statistics data for a set of junctions by analyzingthe path data. Summary statistics data can include one or more valuesassociated with vehicle speed and/or vehicle direction, such as anaverage vehicle stop time at a junction, an average vehicle speed at oneor more geographic coordinates associated with a junction, a maximumvehicle speed at a junction, a minimum vehicle speed at junction, atotal number of vehicles traveling through the junction in a particulardirection (e.g., to identify a number of vehicles turning left, turningright, traveling straight, etc.), and/or the like.

In this way, the location management platform can determine path dataand summary statistics data that can be further processed to train adata model.

As shown in FIG. 1B, and by reference number 120, the locationmanagement platform can train a data model. For example, the locationmanagement platform can train a data model using machine learning. Inthis case, the location management platform can train the data model byassociating the base map data, the path data, and the summary statisticsdata with one or more training values. The one or more training valuescan indicate a likelihood of a traffic control mechanism being locatedat a junction. In this case, the associations between the trainingvalues and the base map data, the path data, and the summary can beprocessed for each junction within the first geographic region, causingthe data model to output a classification identifying a traffic controlmechanism (or lack thereof) for each directional pairing (e.g.,north-south, east-west) associated with each junction within the firstgeographic region.

Shown as an example, the location management platform can associate oneor more training values with path data and summary statistics data ofjunction A. For example, the location management platform can employ aclassification system where a 5 indicates a highest likelihood of ajunction including a particular traffic control mechanism, and a 1indicates a lowest likelihood of a junction including a particulartraffic control mechanism. In this case, the one or more training valuescan indicate a likelihood of a traffic light being located at junctionA, and a likelihood of a stop sign being located at junction A.

In this example, to determine whether a traffic light is located atjunction A, the location management platform can associate the number oflanes per direction (e.g., two) with a training value of 3, associatethe average vehicle stop time of 11 seconds with a training value of 5,associate the average vehicle speed of 20 miles per hour with a trainingvalue of 5, and associate the functional road class of 2 with a trainingvalue of 4. To illustrate the logic used in configuring training values,an average vehicle stop time of 11 seconds can be a strong indicator ofjunction A having a traffic light (e.g., because the average vehiclestop time would be much lower if junction A had a stop sign, a yieldsign, no traffic control mechanism, etc.). As such, the value can beassociated with a training value of 5.

Additionally, to determine whether a stop sign is located at junction A,the location management platform can associate the two lanes perdirection with a training value of 3, associate the average vehicle stoptime of 11 seconds with a training value of 1, associate the averagevehicle speed of 20 miles per hour with a training value of 1, andassociate the functional road class of 2 with a training value of 1. Toillustrate the logic used in configuring training values, an averagevehicle stop time of 11 seconds can be a weak indicator of junction Ahaving a stop sign (e.g., because the average vehicle stop time would bemuch lower if junction A had a stop sign). As such, the value can beassociated with a training value of 1.

In this way, the location management platform can train a data modelthat can be used in determining whether a junction includes a particulartraffic control mechanism.

As shown in FIG. 1C, and by reference number 125, the locationmanagement platform can obtain, from the base map provider device,additional base map data for a second geographic region (e.g., an entirestate, an entire country, a different city, etc.). For example, thelocation management platform can obtain additional base map data so thatthe data model can be used to identify traffic control mechanisms withinthe second geographic region.

As shown by reference number 130, the location management platform canobtain, from the location aware devices, additional observation data fora set of vehicles traveling within the second geographic region. Asshown by reference number 135, the location management platform candetermine additional path data and additional summary statistics data inthe same manner described above.

In this way, the location management platform can use the data model toprocess the additional base map data, the additional path data, and theadditional summary statistics data to identify traffic controlmechanisms at a set of junctions included in the second geographicregion.

As shown in FIG. 1D, and by reference number 140, the locationmanagement platform can determine traffic control mechanisms associatedwith a set of junctions using the data model. For example, the locationmanagement platform can provide the additional base map data, theadditional path data, and the additional summary statistics data asinput for the data model, which can cause the data model to output, foreach junction of the set of junctions, a classification identifying atraffic control mechanism (or lack thereof) used for a particulardirection of a junction.

As shown by reference number 145, the location management platform cangenerate a base map. For example, the location management platform cangenerate a base map that identifies one or more traffic controlmechanisms at junctions using the output of the data model. In thiscase, information identifying traffic control mechanisms at junctionscan be stored as metadata. Shown as an example, the base map can be agraph data structure that includes nodes and edges, where the nodes arejunctions and the edges are geographic areas between the junctions. Inthis case, the information identifying traffic control mechanisms can bestored as node metadata.

As shown by reference number 150, the location management platform canreceive, from a user device, a request for a set of navigationaldirections. For example, the location management platform can receive arequest for a set of navigational directions that includes informationindicating an arrival location and information indicating a destinationlocation.

As shown by reference number 155, the location management platform canprovide, to the user device, a set of navigational directions associatedwith a best-fit path. For example, the location management platform canuse the base map and one or more path-finding algorithms to identify abest-fit path (e.g., a fastest path, a safest path, a mostfuel-efficient path, etc.). In this case, the base map can enable theone or more path-finding algorithms to identify a best-fit path due tothe base map including information identifying traffic controlmechanisms. Furthermore, the location management platform can providethe set of navigational directions associated with the best-fit path tothe user device.

In this way, the location management platform is able to use theinformation identifying the traffic control mechanisms to identify abest-fit path for a user. By identifying the best-fit path for the user,the location management platform conserves processing resources relativeto devices that identify an inefficient path and might need torecalculate a path mid-route, conserves natural resources (e.g.,conservation of fuel), improves driver safety, and/or the like.

As indicated above, FIGS. 1A-1D are provided merely as an example. Otherexamples are possible and can differ from what was described with regardto FIGS. 1A-1D. For example, in other implementations, the locationmanagement platform can use the base map to provide clearer voicemessages when providing navigational directions, more accuratedestination arrival time predictions, traffic infrastructuremodifications (e.g., by recommending to build a new traffic controlmechanism, by recommending to modify or by modifying a traffic lightsignal timer, etc.), and/or the like.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, can be implemented. As shown in FIG.2, environment 200 can include location aware devices 210, base mapprovider device 220, location management platform 230 hosted withincloud computing environment 240, user device 250, and/or network 260.Devices of environment 200 can interconnect via wired connections,wireless connections, or a combination of wired and wirelessconnections.

Location aware devices 210 include one or more devices capable ofobtaining, monitoring, storing, and/or providing observation data. Forexample, location aware devices 210 can include a sensor (e.g., avehicle sensor, a sensor located at the intersection, etc.), a camera(e.g., a traffic camera, a vehicle camera, etc.), a device associatedwith a vehicle, such as a smart phone, a speed detecting device, aglobal positioning system (GPS) receiver device, and/or any other devicecapable of obtaining, monitoring, storing, and/or providing observationdata.

Base map provider device 220 includes one or more devices capable ofreceiving, storing, processing, and/or providing base map data. Forexample, base map provider device 220 can include a server or a group ofservers. In some implementations, base map provider device 220 canreceive a request, from location management platform 230, for base mapdata, which can cause base map provider device 220 to provide the basemap data to location management platform 230. In some implementations,base map provider device 220 can be configured to automatically providebase map data to location management platform 230.

Location management platform 230 includes one or more devices capable ofreceiving, storing, processing, and/or providing data associated with aset of junctions. For example, location management platform 230 caninclude a computing device, such as a server device (e.g., a hostserver, a web server, an application server, etc.), a data centerdevice, or a similar device. In some implementations, locationmanagement platform 230 can obtain base map data and/or additional basemap data from base map provider device 220. In some implementations,location management platform 230 can obtain observation data and/oradditional observation data from location aware devices 210. In someimplementations, location management platform 230 can provide a set ofnavigational directions associated with a best-fit path to user device250.

In some implementations, as shown, location management platform 230 canbe hosted in cloud computing environment 240. Notably, whileimplementations described herein describe location management platform230 as being hosted in cloud computing environment 240, in someimplementations, location management platform 230 might not becloud-based (i.e., can be implemented outside of a cloud computingenvironment) or can be partially cloud-based.

Cloud computing environment 240 includes an environment that hostslocation management platform 230. Cloud computing environment 240 canprovide computation, software, data access, storage, and/or otherservices that do not require end-user knowledge of a physical locationand configuration of system(s) and/or device(s) that host locationmanagement platform 230. As shown, cloud computing environment 240 caninclude a group of computing resources 232 (referred to collectively as“computing resources 232” and individually as “computing resource 232”).

Computing resource 232 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource232 can host location management platform 230. The cloud resources caninclude compute instances executing in computing resource 232, storagedevices provided in computing resource 232, data transfer devicesprovided by computing resource 232, etc. In some implementations,computing resource 232 can communicate with other computing resources232 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 232 includes a group ofcloud resources, such as one or more applications (“APPs”) 232-1, one ormore virtual machines (“VMs”) 232-2, virtualized storage (“VSs”) 232-3,one or more hypervisors (“HYPs”) 233-4, or the like.

Application 232-1 includes one or more software applications that can beprovided to or accessed by location aware devices 210, base map providerdevice 220, and/or user device 250. Application 232-1 can eliminate aneed to install and execute the software applications on location awaredevices 210, base map provider device 220, and/or user device 250. Forexample, application 232-1 can include software associated with locationmanagement platform 230 and/or any other software capable of beingprovided via cloud computing environment 240. In some implementations,one application 232-1 can send/receive information to/from one or moreother applications 232-1, via virtual machine 232-2.

Virtual machine 232-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 232-2 can be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 232-2. A system virtual machinecan provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine can executea single program, and can support a single process. In someimplementations, virtual machine 232-2 can manage infrastructure ofcloud computing environment 240, such as data management,synchronization, or long-duration data transfers.

Virtualized storage 232-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 232. In someimplementations, within the context of a storage system, types ofvirtualizations can include block virtualization and filevirtualization. Block virtualization can refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem can be accessed without regard to physical storage orheterogeneous structure. The separation can permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization can eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This can enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 232-4 can provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 232.Hypervisor 232-4 can present a virtual operating platform to the guestoperating systems, and can manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems can sharevirtualized hardware resources.

User device 250 includes one or more devices capable of receiving,storing, processing, and/or providing information associated withnavigational directions. For example, user device 250 can include adevice associated with a vehicle, such as a smart phone, a speeddetecting device, a GPS receiver device, and/or any other device capableof receiving, storing, processing, and/or providing informationassociated with navigational directions. In some implementations, userdevice 250 can provide, to location management platform 230, a requestfor a set of navigational directions. In some implementations, userdevice 250 can receive, from location management platform 230, a set ofnavigational directions associated with a best-fit path. In someimplementations, user device 250 can be location aware device 210.

Network 260 includes one or more wired and/or wireless networks. Forexample, network 260 can include a cellular network (e.g., a fifthgeneration (5G) network, a fourth generation (4G) network, such as along-term evolution (LTE) network, a third generation (3G) network, acode division multiple access (CDMA) network, another type of advancedgenerated network, etc.), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the Public Switched TelephoneNetwork (PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there can be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 can beimplemented within a single device, or a single device shown in FIG. 2can be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 can perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300can correspond to location aware devices 210, base map provider device220, location management platform 230 hosted within cloud computingenvironment 240, and/or user device 250. In some implementations,location aware devices 210, base map provider device 220, locationmanagement platform 230 hosted within cloud computing environment 240,and/or user device 250 can include one or more devices 300 and/or one ormore components of device 300. As shown in FIG. 3, device 300 caninclude a bus 310, a processor 320, a memory 330, a storage component340, an input component 350, an output component 360, and acommunication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320includes a central processing unit (CPU), a graphics processing unit(GPU), an accelerated processing unit (APU), a microprocessor, amicrocontroller, a digital signal processor (DSP), a field-programmablegate array (FPGA), an application-specific integrated circuit (ASIC), oranother type of processing component. In some implementations, processor320 includes one or more processors capable of being programmed toperform a function. Memory 330 includes a random access memory (RAM), aread only memory (ROM), and/or another type of dynamic or static storagedevice (e.g., a flash memory, a magnetic memory, and/or an opticalmemory) that stores information and/or instructions for use by processor320. In some implementations, memory 330 can include one or morememories.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 caninclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 caninclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 can permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 can include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 can perform one or more processes described herein. Device300 can perform these processes in response to processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions can be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 can causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry can be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 can include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 canperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for performing one ormore actions associated with improving vehicle navigation and/or trafficmanagement by generating and using a base map that includes informationidentifying traffic control mechanisms at one or more junctions in ageographic region. In some implementations, one or more process blocksof FIG. 4 can be performed by location management platform 230. In someimplementations, one or more process blocks of FIG. 4 can be performedby another device or a group of devices separate from or includinglocation management platform 230, such as location aware devices 210,base map provider device 220, cloud computing environment 240, and userdevice 250.

As shown in FIG. 4, process 400 can include obtaining base map dataassociated with a first geographic region (block 410). For example,location management platform 230 can obtain base map data associatedwith a first geographic region from base map provider device 220. Thebase map data can include a set of values indicating attributes of aroad map, such as geographic coordinates of junctions, geographiccoordinates of areas between the junctions, functional road classes,speed limits, a number of lanes associated with a road, time stamps(e.g., indicating a most recent time data included in the base map wasupdated), safety information (e.g., indicating high crime areas, lowcrime areas, etc.), construction information (e.g., identifying areasthat are under construction), accident information (e.g., lane closures,road closures, etc.), and/or the like.

In some implementations, location management platform 230 can obtainbase map data. For example, location management platform 230 can obtainbase map data by providing a request (e.g., to base map provider device220), by receiving base map data automatically (e.g., periodically overa time interval), or the like. Additionally, location managementplatform 230 can obtain or receive the base map data in a singletransmission, in separate portions using multiple transmissions, or thelike.

In some implementations, location management platform 230 can store basemap data using a data structure. For example, location managementplatform 230 can store base map data using a graph data structure. Inthis case, the graph data structure can include one or more nodes andone or more edges. A node can identify a geographic location, such as ageographic location of a junction. An edge can identify a geographicarea between nodes, such as a portion of a road between two junctions.Furthermore, the nodes and/or the edges can include metadata for storingbase map data, such as metadata indicating a functional road class, aspeed limit, and/or the like.

Alternatively, location management platform 230 can store base map datausing a different data structure, such as a linked-list, an array, ahash table, and/or the like. In some implementations, locationmanagement platform 230 can use a data structure that includesthousands, tens of thousands, hundreds of thousands, or even millions(or more) of data points. In this way, location management platform 230is able to store large quantities data, such that a human operator or aninferior cloud service provider can be objectively unable to analyze orprocess.

In this way, location management platform 230 is able to obtain base mapdata that can be processed in conjunction with path data and summarystatistics data to train a data model, as described further herein.

As further shown in FIG. 4, process 400 can include determining pathdata for vehicles traveling within the first geographic region andsummary statistics data for a set of junctions within the firstgeographic region (block 420). For example, location management platform230 can obtain observation data from location aware devices 210 includedwithin the first geographic region, can analyze the observation data todetermine path data, and can analyze the path data to determine summarystatistics data.

In some implementations, location management platform 230 can obtainobservation data. For example, location aware devices 210 can monitor aset of vehicles traveling within the first geographic region, and canreport observation data associated with the set of vehicles to locationmanagement platform 230. The observation data can include a set ofvalues used to identify one or more locations of vehicles at one or moretime periods, such as a geographic location of the vehicle, a timestamp, a vehicle identifier, a vehicle speed, and/or the like.

In some implementations, location management platform 230 can determinepath data. For example, location management platform 230 can determinepath data for a set of vehicles traveling within the first geographicregion. Path data can include information identifying a path that avehicle travels within the first geographic region and can be determinedby creating logical connections between sets of geographic coordinatesincluded in the observation data. Additionally, path data can includeadditional information associated with the vehicle, such as informationindicating a vehicle speed, information indicating a vehicle direction,and/or the like. For example, location aware devices 210 might notreport vehicle speed and/or vehicle direction, and location managementplatform 230 can determine vehicle speed and/or vehicle direction usinggeographic coordinates and time stamps.

In some implementations, location management platform 230 can determinesummary statistics data. For example, location management platform 230can determine summary statistics data for a set of junctions byanalyzing the path data. In this case, location management platform 230can analyze, for each vehicle traveling through each junction of the setof junctions, vehicle speed, vehicle direction, time stamps, geographiccoordinates, and/or the like, to determine summary statistics data forthe junctions. Summary statistics data can include one or more valuesassociated with vehicle speed and/or vehicle direction, such as anaverage vehicle stop time at a junction, an average vehicle speed at oneor more geographic coordinates associated with a junction, a maximumvehicle speed at a junction, a minimum vehicle speed at junction, atotal number of vehicles traveling through the junction in a particulardirection (e.g., to identify a number of vehicles turning left, turningright, traveling straight, etc.), and/or the like.

In some cases, location management platform 230 can determine summarystatistics data only for straight line paths. For example, if path dataincludes a first set of geographic coordinates for a time when a vehicleenters a junction, and a second set of geographic coordinates for a timewhen a vehicle exits a junction, location management platform 230 cancompare the first set of geographic coordinates and the second set ofgeographic coordinates to determine whether a vehicle traveled straightthrough the junction or turned at the junction. In this case, locationmanagement platform 230 can determine summary statistics data only ifthe vehicle traveled straight through the intersection.

In the case of determining summary statistics data only for straightline paths, location management platform 230 is able to make moreaccurate identifications of traffic control mechanisms. For example,vehicle wait time can be used to determine whether a junction hastraffic lights, stop signs, or neither, and paths where a vehicle turnsat a junction can cause inconsistent data (e.g., left hand turns ofteninvolve a variable turn time). In this way, location management platform230 conserves processing resources relative to determining summarystatistics for all paths.

In some implementations, location management platform 230 can store pathdata and/or summary statistics data using the graph data structure. Forexample, location management platform 230 can store path data and/orsummary statistics data in metadata associated with the nodes and/or theedges of the graph data structure. As an example, path data for a set ofvehicles can include information associated with a particular junction,and location management platform 230 can determine summary statisticsfor the junction to identify an average vehicle speed and an averagevehicle wait time at the junction (these can be determined for eachdirection). In this case, node metadata associated with the particularjunction can store values indicating the average vehicle speed and theaverage vehicle wait time. In this way, nodes associated with junctionscan include metadata that can be used to train a data model, asdescribed further herein.

In this way, location management platform 230 can use base map data,plan data, and summary statistics data to train a data model.

As further shown in FIG. 4, process 400 can include training a datamodel using the base map data, the path data, and the summary statisticsdata (block 430). For example, location management platform 230 can useone or more machine learning techniques to train a data model. In thiscase, the data model can be trained to identify traffic controlmechanisms (e.g., a traffic light, a stop sign, a yield sign, etc.)located at one or more junctions of the set of junctions.

In some implementations, location management platform 230 canstandardize the base map data, the path data, and the summary statisticsdata. For example, location management platform 230 can standardize thebase map data, the path data, and the summary statistics data to allowthe information to be processed to train a data model. In this case, thebase map data, the path data, and the summary statistics data can beassociated with different file types, different file formats, and/or thelike, and location management platform 230 can apply a standardizationtechnique to allow the information to be stored in a uniform format. Insome implementations, location management platform 230 can applydifferent standardization techniques for different file types and/orfile formats. By standardizing the base map data, the path data, and thesummary statistics data, location management platform 230 can use theinformation to train a data model.

In some implementations, as described herein, location managementplatform 230 can train a data model using a supervised machine learningtechnique. Additionally, or alternatively, location management platform230 can train a data model using a different type of machine learningtechnique, such as machine learning via clustering, dimensionalityreduction, structured prediction, anomaly detection, neutral networks,reinforcement learning, or the like.

In some implementations, location management platform 230 can train adata model by associating the base map data, the path data, and thesummary statistics data with one or more training values. For example,location management platform 230 can associate base map data, path data,and summary statistics data for each junction of the set of junctionsincluded in the first geographic region with the one or more trainingvalues. A training value can indicate a likelihood of a traffic controlmechanism being located at a junction. By associating base map data,path data, and summary statistics data with one or more training values,location management platform 230 can use a data model to predict whethertraffic control mechanisms are located at the set of junctions as wellas which traffic control mechanisms are located at the set of junctions.

In some cases, location management platform 230 can employ aclassification scale that uses a training value of 1 to indicate alowest likelihood of a junction including a particular traffic controlmechanism, and a training value of 5 to indicate a highest likelihood ofa junction including a particular traffic control mechanism. Forexample, particular values included in the base map data, the path data,and summary statistics data can be associated with particular trainingvalues based on a degree of likelihood of the particular valuesindicating a particular traffic control mechanism (or lack thereof). Inthis case, location management platform 230 can establish associationsbetween training values and input values (base map data, path data,summary statistics data) for different types of traffic controlmechanisms that can be located at junctions (e.g., a traffic light, astop sign, a yield sign, no traffic control mechanism, etc.).

As an example, assume base map data for a first road passing through afirst junction indicates a functional road class of 1 (e.g., a highway),and that base map data for a second road passing through a secondjunction indicates a functional road class of 5 (e.g., a residentialstreet). In this case, location management platform 230 can train thedata model by associating a value indicating a functional road class of1 with a training value of 1 for a traffic light category (e.g., becausea junction at a highway is unlikely to include a traffic light), atraining value of 1 for a stop sign category (e.g., because a junctionat a highway is unlikely to include a stop sign), and a training valueof 5 for a no traffic control mechanism category (e.g., because ajunction at a highway is likely to have no traffic control mechanism).

In the case of functional road class 5 (e.g., the residential street),location management platform 230 can associate functional road class 5with a training value of 1 for a traffic light category (e.g., because ajunction at a residential street is unlikely to have a traffic light), atraining value of 4 for a stop sign category (e.g., because a junctionat a residential street is likely to have a stop sign), and a trainingvalue of 4 for a no traffic control mechanism category (e.g., because ajunction at a residential street is likely to have no traffic controlmechanism).

As the example illustrates, training values associated with one categorymight be insufficient to provide conclusive evidence of whether aparticular junction includes a particular traffic control mechanism. Assuch, location management platform 230 can train the data model byassociating training values with one or more additional values includedin the base map data, the path data, and/or the summary statistics data,such average vehicle wait time at a junction, average vehicle speed at ajunction, maximum vehicle speed at a junction, minimum vehicle speed ata junction, vehicle direction at a junction, and/or the like.

In some implementations, location management platform 230 can use theassociated values for a junction to output a classification identifyinga traffic control mechanism (or lack thereof). For example, locationmanagement platform 230 can, for each direction associated with eachjunction within the first geographic region, classify the junctions ashaving a traffic light, a stop sign, a yield sign, no traffic controlmechanism, or the like.

In some implementations, location management platform 230 can assignweights to the associated values to allow the data model to output aclassification identifying a traffic control mechanism. For example,particular values included in the base map data, the path data, and thesummary statistics data can serve as a stronger or a weaker indicator ofwhether a junction includes a particular traffic control mechanism, andlocation management platform 230 can assign weights to the particularvalues to improve accuracy of a classification that the data model canoutput.

As an example, assume average vehicle wait time and average vehiclespeed are stronger indications of a particular traffic control mechanismthan functional road class. In this case, an average vehicle wait timecan be associated with a weight of, for example, 0.4, an average vehiclespeed can be associated with a weight of, for example, 0.4, and afunctional road class can be associated with a weight of, for example,0.2. In this way, location management platform 230 can use weightedvalues to allow the data model to output a classification identifying atraffic control mechanism.

In some implementations, location management platform 230 can validatethe data model. For example, location management platform 230 canvalidate the data model by using test information as input for the datamodel. In this case, location management platform 230 can obtain testinformation associated with a set of junctions with known trafficcontrol mechanisms. Here, location management platform 230 can providethe test information as input for the data model, which can cause thedata model to output one or more classifications. Additionally, locationmanagement platform 230 can validate the data model by determiningwhether the output of the data model satisfies a threshold level ofaccuracy.

In some implementations, location management platform 230 can retrainthe data model. For example, if a validation check determines that adata model does not satisfy a threshold level of accuracy, then locationmanagement platform 230 can retrain the data model. In some cases,location management platform 230 can retrain the data model by providingtraining values associated with a junction that is incorrectlyclassified to a device accessible by a domain expert.

Alternatively, location management platform 230 can retrain the datamodel automatically. For example, location management platform 230 canretrain the data model automatically by flagging the training valuesassociated with a junction that is incorrectly classified, and bymodifying the one or more flagged training values. In this case,location management platform 230 can test the junction over an extendedtime period to collect a larger data set. By modifying training valuesand/or testing the junction over an extended time period to collect alarger data set, location management platform 230 can determineadditional path data and/or additional summary statistics data needed tomake a more accurate classification.

In some implementations, location management platform 230 can receive atrained data model. For example, a device external to locationmanagement platform 230 can train a data model, or a portion of the datamodel, in the same manner described above, and the device can providethe trained data model, or the portion of the trained data model, tolocation management platform 230. In this way, location managementplatform 230 can, but does not need to be, the device training the datamodel.

In this way, location management platform 230 is able to train a datamodel using the base map data, the path data, and the summary statisticsdata.

As further shown in FIG. 4, process 400 can include determining trafficcontrol mechanisms associated with a set of junctions within a secondgeographic region by providing additional base map data, additional pathdata, and additional summary statistics as input for the data model(block 440). For example, location management platform 230 can obtainadditional base map data, additional path data, and additional summarystatistics data (e.g., for an entire state, an entire country, etc.),and can provide the additional data as input for the data model. In thiscase, the data model can output a set of classifications for the set ofjunctions, where a classification identifies a traffic control mechanism(or lack thereof) used for a particular direction of a junction.

In some implementations, location management platform 230 can determinewhether a junction includes a traffic control mechanism. For example,location management platform 230 can provide additional base map data,additional path data, and additional summary statistics data associatedwith the junction as input to the data model. In this case, the datamodel can output a classification indicating a type of traffic controlmechanism that is located at the junction.

As an example, location management platform 230 can determine that atraffic light is located at a junction. For example, assume a junctionconnects two multi-lane roads (e.g., roads with a functional road classof 2). Further assume the junction has an average vehicle stop time of10 seconds. In this case, the data model can associate a valueindicating a functional road class of 2 with a training value of 4 for atraffic light category, can associate a value indicating an averagevehicle stop time of 10 seconds with a training value of 5 for thetraffic light category, and, based on the associations, determine that atraffic light is located at the junction.

As another example, location management platform 230 can determine thata stop sign is located at a junction. For example, assume a junctionconnects two multi-lane roads (e.g., roads with a functional road classof 3). Further assume that vehicles traveling in a first direction(e.g., north-south) through the junction have an average vehicle stoptime of 2 seconds, and that vehicles traveling in a second direction(e.g., east-west) through the junction have an average vehicle stop timeof 0 seconds. In this case, the data model can associate a valueindicating a functional road class of 3 with a training value of 4 for astop sign category, can associate a value indicating an average vehiclestop time for the first direction (e.g., 2 seconds) with a trainingvalue of 5 for the stop sign category, and can associate a valueindicating an average vehicle stop time for the second direction (e.g.,0 seconds) with a training value of 1 for the stop sign category. Here,based on the associations, location management platform 230 candetermine that a stop sign is located at the junction for the firstdirection, and that no traffic control mechanism is located at thejunction for the second direction.

In this way, location management platform 230 can determine trafficcontrol mechanisms associated with a set of junctions by providingadditional base map data, additional path data, and additional summarystatistics as input for the data model.

As further shown in FIG. 4, process 400 can include generating a basemap that includes information identifying the traffic control mechanismsat one or more junctions of the set of junctions included within thesecond geographic region (block 450). For example, location managementplatform 230 can generate a base map (e.g., a graph data structure) thatincludes metadata (e.g., node metadata) identifying the traffic controlmechanisms at one or more junctions of the set of junctions.

In some implementations, location management platform 230 can generate abase map using a graph data structure. For example, location managementplatform 230 can generate a graph data structure that includes nodes,edges, and metadata. In this case, the graph data structure can includea set of nodes indicating junctions, a set of edges indicatinggeographic areas between the junctions, and metadata to identify thetraffic control mechanisms (or lack thereof). Alternatively, locationmanagement platform 230 can generate a base map using another datastructure, such as a linked-list, an array, a hash table, a tree, or thelike.

In this way, location management platform 230 can generate a base mapthat includes information identifying the traffic control mechanisms atone or more junctions within the second geographic region.

As further shown in FIG. 4, process 400 can include performing one ormore actions associated with improving vehicle navigation and/or trafficmanagement (block 460). For example, location management platform 230can, after generating the base map, perform one or more actionsassociated with improving vehicle navigation, such as provide, to a userdevice, a set of navigational instructions that identify a best-fitpath, a set of navigational instructions that can be output using voicemessages, a destination arrival time prediction, and/or the like.Additionally, or alternatively, location management platform 230 can,after generating the base map, perform one or more actions associatedwith improving traffic management, such as generating a recommendationto install or remove a traffic control mechanism, generating arecommendation to modify a setting of a traffic control mechanism (e.g.,a signal timer on a traffic light), and/or the like.

In some implementations, location management platform 230 can determinea set of navigational directions to identify a best-fit path. Forexample, location management platform 230 can receive a request for aset of navigational directions, determine the set of navigationaldirections to identify a best-fit path (e.g., a fastest path, a safestpath, a most fuel-efficient path, etc.), and provide the set ofnavigational directions to user device 250.

In some implementations, location management platform 230 can receive arequest for a set of navigational directions. For example, locationmanagement platform 230 can receive, from user device 250, a request fora set of navigational directions that includes information indicating anarrival location and information indicating a destination location. Insome cases, the request can include a request to determine a fastestpath, a safest path, a most fuel-efficient path, or the like.

In some implementations, location management platform 230 can determinea best-fit path for user device 250. For example, location managementplatform 230 can use the base map and one or more path-findingalgorithms (e.g., a fastest path algorithm, a safest path algorithm, amost fuel-efficient path algorithm, etc.) to identify a best-fit path.In this case, location management platform 230 can allow thepath-finding algorithms to reference metadata identifying trafficcontrol mechanisms, thereby ensuring that the path-finding algorithmsare able to determine a best-fit path.

In some implementations, location management platform 230 can providethe set of navigational directions to user device 250. For example,location management platform can provide, to user device 250, the set ofnavigational directions identifying the best-fit path.

In some implementations, location management platform 230 can determinea fastest path for user device 250. For example, location managementplatform 230 can use the base map (which identifies traffic controlmechanisms) and a fastest path algorithm to determine a fastest path. Inthis case, the fastest path can be a path with a least amount of stops,and location management platform 230 can select the path with the leastamount of stops as a result of the base map identifying the trafficcontrol mechanisms (or lack thereof).

In some implementations, location management platform 230 can determinea safest path for user device 250. For example, location managementplatform 230 can use the base map (which includes safety information)and a safest path algorithm to determine a safest path. In this case,the safest path can avoid high crime areas, avoid intersections withtraffic lights (e.g., in an area with a high crime rate, stopping attraffic lights for extended time periods can be dangerous), and/or thelike.

In some implementations, location management platform 230 can determinea most fuel-efficient path for user device 250. For example, locationmanagement platform 230 can use the base map (which identifies trafficcontrol mechanisms) and a most fuel-efficient path algorithm todetermine a most fuel-efficient path. In this case, location managementplatform 230 can select a path with a least amount of stops (e.g.,thereby conserving fuel) by selecting a path that avoids all (or some)traffic control mechanisms.

Additionally, or alternatively, location management platform 230 can usethe base map to provide a set of navigational directions that can beoutput via voice messages. For example, location management platform 230can receive a request for a set of navigational directions, determinethe set of navigational directions, and provide the set of navigationaldirections to user device 250. In this case, user device 250 can outputthe set of navigational directions using voice messages that identifyone or more traffic control mechanisms in the navigational directions.In some cases, the identified traffic control mechanisms can be used aslandmarks to make it easier for a user to understand the navigationaldirections.

As an example, a voice message in a set of navigational directions thatuses the base map can state “Turn left at the traffic light on MainStreet,” whereas a voice message without access to the base map mightstate “In 500 feet, turn Left on Main Street.” This reduces a risk ofuser error as a user can more readily identify a traffic light than astreet that is 500 feet away. By using the identified traffic light inthe voice message, location management platform 230 is able to providenavigational directions that are easier for a user to understand.Additionally, by providing a user with easier to understand navigationaldirections, a user is less likely to take an incorrect path. Thisconserves processing resources and/or network resources that mightotherwise be used to recalculate navigational directions.

Additionally, or alternatively, location management platform 230 can usethe base map to determine a destination arrival time for a set ofnavigational directions. For example, assume location managementplatform 230 receives a request for a set of navigational directions.Further assume location management platform 230 identifies a best-fitpath for a user. In this case, location management platform 230 candetermine a destination arrival time by analyzing base map metadataassociated with one or more junctions in the identified best-fit path(e.g., metadata indicating an average vehicle stop time at a junction,an average vehicle speed at one or more geographic coordinates of thejunction, etc.). In this way, location management platform 230 candetermine a destination arrival time that is more accurate than adestination arrival time that is determined without the metadata of thebase map.

In some implementations, location management platform 230 can generate arecommendation to install, remove, or make a modification to a trafficcontrol mechanism. For example, location management platform 230 cananalyze metadata for a set of junctions to determine whether metadatafor one or more junctions satisfy a threshold (e.g., a thresholdassociated with traffic volume, a threshold associated with vehiclespeed, etc.). In this case, location management platform 230 cangenerate a recommendation to install, remove, or modify a trafficcontrol mechanism based on determining whether the one or more junctionssatisfy the threshold.

As an example, assume location management platform 230 determinessummary statistics data indicating an average vehicle speed of 30 milesper hour through a junction in a residential neighborhood. Furtherassume that a threshold associated with recommending to install stopsigns in residential areas is associated with an average vehicle speedof 28 miles per hour. In this case, because the average vehicle speedsatisfies the threshold speed, and because location management platform230 identifies that there is no traffic control mechanism at thejunction, location management platform 230 can recommend to add a stopsign at the junction.

As another example, location management platform 230 can generate arecommendation to modify a setting of a traffic control mechanism. Inthis example, assume location management platform 230 receivesadditional base map data, additional path data, and/or additionalsummary statistics data. In this case, location management platform 230can analyze the additional base map data, the additional path data,and/or the additional summary statistics data for a junction todetermine whether traffic at the junction satisfies a threshold level ofcongestion. In some cases, if traffic at a junction that is associatedwith a first direction (e.g., north-south) satisfies the threshold levelof congestion, and traffic at the junction that is associated with asecond direction (e.g., east-west) does not satisfy the threshold levelof congestion, then location management platform 230 can generate arecommendation to modify a setting of the traffic control mechanism(e.g., a recommendation to increase or decrease a traffic light signaltime associated with the first direction or the second direction).

In some implementations, location management platform 230 can provide arecommendation to install, update, and/or remove a traffic controlmechanism to a device associated with an interested party. For example,location management platform 230 can provide the recommendation to adevice associated with a city department of transportation, a deviceassociated with a commercial provider that manufactures, installs,and/or removes traffic control mechanisms, and/or the like.

In this way, location management platform 230 can, after generating thebase map, determine a set of navigational directions identifying abest-fit path for user device 250.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 can include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 can be performed in parallel.

In this way, location management platform 230 is able to identifytraffic control mechanisms in a geographic region and use knowledge ofthe location of the traffic control mechanisms to identify a best-fitpath for a user. By identifying the best-fit path for the user, locationmanagement platform 230 conserves processing resources relative todevices that are unable to utilize a base map that includes trafficcontrol mechanisms, conserves natural resources (e.g., conservation offuel), improves driver safety, and/or the like.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or can be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold can refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, or the like.

To the extent the aforementioned embodiments collect, store, or employpersonal information provided by individuals, it should be understoodthat such information shall be used in accordance with all applicablelaws concerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

It will be apparent that systems and/or methods, described herein, canbe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features can be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below can directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and can be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and can be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: a memory; and one or moreprocessors to: obtain base map data associated with a first geographicregion, the base map data including a set of values indicatingattributes of a road map, and the base map data not including trafficcontrol mechanisms; determine summary statistics data for a set ofjunctions within the first geographic region, the summary statisticsdata including information associated with a set of vehicles travelingthrough the set of junctions; train a data model using the base map dataand the summary statistics data, where the one or more processors, whentraining the data model, are to: associate the base map data and thesummary statistics data from a junction of the set of junctions with oneor more training values,  the one or more training values including alikelihood of a traffic control mechanism being located at the junction, the traffic control mechanism including a traffic light, a stop sign,or a yield sign; and classify the junction as having a particulartraffic control mechanism based on associating the base map data and thesummary statistics data with the one or more training values; obtain,after training the data model, additional base map data associated witha second geographic region; determine additional summary statistics datafor a set of junctions within the second geographic region; determinetraffic control mechanisms associated with the set of junctions withinthe second geographic region by providing the additional base map dataand the additional summary statistics data as input for the data model;generate, using output of the data model, a base map that includesinformation identifying the traffic control mechanisms at one or morejunctions of the set of junctions included within the second geographicregion; and perform, after generating the base map, one or more actionsassociated with improving vehicle navigation or traffic management. 2.The device of claim 1, where the set of values indicating attributes ofthe road map include at least one of: one or more values indicatinggeographic coordinates of the set of junctions included in the firstgeographic region, one or more values indicating geographic coordinatesof areas between the set of junctions included in the first geographicregion, one or more values indicating functional road classes, one ormore values indicating speed limits, or one or more values indicating anumber of lanes associated with the set of junctions included within thefirst geographic region.
 3. The device of claim 1, where the summarystatistics data for a junction of the set of junctions includes at leastone of: a value indicating an average vehicle stop time at the junction,a value indicating an average vehicle speed at one or more geographiccoordinates associated with the junction, a value indicating a maximumvehicle speed at the junction, a value indicating a minimum vehiclespeed at the junction, or a value indicating a total number of vehiclestraveling through the junction in a particular direction.
 4. The deviceof claim 1, where the one or more processors, when determining thesummary statistics data, are to: obtain observation data for the set ofvehicles traveling through the set of junctions, the observation dataidentifying one or more locations of the set of vehicles at one or moretime periods, determine path data for the set of vehicles by analyzingthe observation data, the path data including information indicatingvehicle speed and information indicating vehicle direction, anddetermine the summary statistics data by analyzing the path data for theset of vehicles.
 5. The device of claim 1, where the one or moreprocessors, when determining the traffic control mechanisms, are to:provide the additional base map data and the additional summarystatistics data as input for the data model to cause the data model tooutput a set of classifications for the set of junctions within thesecond geographic region, the set of classifications indicating whetherthe set of junctions within the second geographic region include trafficcontrol mechanisms; and where the one or more processors, whengenerating the base map, are to: generate a data structure indicative ofthe base map, the data structure including metadata identifying thetraffic control mechanisms at one or more junctions of the set ofjunctions within the second geographic region, the metadata being basedon the set of classifications indicating whether the set of junctionswithin the second geographic region include traffic control mechanisms.6. The device of claim 1, where the one or more processors, whenperforming the one or more actions, are to: receive a request from auser device for a set of navigational directions, determine a set ofnavigational directions by using a path-finding algorithm to analyze thebase map, and provide the set of navigational directions to the userdevice.
 7. The device of claim 1, where the one or more processors, whenperforming one or more actions associated with improving trafficmanagement, are to: analyze metadata for the set of junctions todetermine whether metadata for one or more junctions of the set ofjunctions satisfies a threshold level of congestion; and generate arecommendation to install or remove a traffic control mechanism based onthe one or more junctions satisfying the threshold level of congestion.8. A non-transitory computer-readable medium storing instructions, theinstructions comprising: one or more instructions that, when executed byone or more processors, cause the one or more processors to: obtain basemap data associated with a first geographic region, the base map dataincluding a set of values indicating attributes of a road map, and thebase map data not including traffic control mechanisms; determinesummary statistics data for a set of junctions within the firstgeographic region, the summary statistics data including informationassociated with a set of vehicles traveling through the set ofjunctions; train a data model using the base map data and the summarystatistics data, where the one or more instructions, that cause the oneor more processors to train data model, cause the one or more processorsto: associate the base map data and the summary statistics data from ajunction of the set of junctions with one or more training values,  theone or more training values including a likelihood of a traffic controlmechanism being located at the junction,  the traffic control mechanismincluding a traffic light, a stop sign, or a yield sign; and classifythe junction as having a particular traffic control mechanism based onassociating the base map data and the summary statistics data with theone or more training values; obtain, after training data model,additional base map data associated with a second geographic region;determine additional summary statistics data for a set of junctionswithin the second geographic region; determine traffic controlmechanisms associated with the set of junctions within the secondgeographic region by providing the additional base map data and theadditional summary statistics data as input for the data model;generate, using output of the data model, a base map that includesinformation indicating whether the set of junctions included within thesecond geographic region include traffic control mechanisms; andperform, after generating the base map, one or more actions associatedwith improving vehicle navigation or traffic management.
 9. Thenon-transitory computer-readable medium of claim 8, where the one ormore instructions, that cause the one or more processors to determinethe additional summary statistics data, cause the one or more processorsto: determine path data for the set of vehicles by analyzing observationdata, the observation data identifying one or more locations of the setof vehicles at one or more time periods, and the path data identifyingone or more paths traveled by the set of vehicles, and determine thesummary statistics data by analyzing the path data for the set ofvehicles.
 10. The non-transitory computer-readable medium of claim 8,where the one or more instructions, when executed by the one or moreprocessors, further cause the one or more processors to: obtain testinformation associated with a set of junctions with known trafficcontrol mechanisms; provide the test information as input for the datamodel; and determine whether output of the data model satisfies athreshold level of accuracy.
 11. The non-transitory computer-readablemedium of claim 10, where the one or more instructions, that cause theone or more processors to determine whether the output of the data modelsatisfies the threshold level of accuracy, cause the one or moreprocessors to: determine that the output of the data model does notsatisfy a threshold level of accuracy; and where the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: flag the one or more training valuesassociated with determining the output of the data model as inaccuratetraining values, modify the one or more training values that areflagged, obtain additional base map data, additional path data, and/oradditional summary statistics data, and retrain the data model byassociating the additional base map data, the additional path data,and/or the additional summary statistics data with the one or moremodified training values.
 12. The non-transitory computer-readablemedium of claim 10, where the one or more instructions, that cause theone or more processors to perform the one or more actions, cause the oneor more processors to: receive a request from a user device for a set ofnavigational directions, determine the set of navigational directionsusing a path-finding algorithm, determine a destination arrival time forthe set of navigational directions by analyzing base map metadata forone or more junctions used in the set of navigational directions, andprovide the destination arrival time to the user device.
 13. Thenon-transitory computer-readable medium of claim 8, where the one ormore instructions, that cause the one or more processors to perform theone or more actions, cause the one or more processors to: receive arequest from a user device for a set of navigational directions,determine the set of navigational directions using a path-findingalgorithm, and provide the set of navigational directions to the userdevice to cause the user device to output the set of navigationaldirections using voice messages, the voice messages including one ormore traffic control mechanisms to be used as landmarks.
 14. A method,comprising: obtaining, by a device, base map data associated with afirst geographic region, the base map data including a set of valuesindicating attributes of a road map, and the base map data not includingtraffic control mechanisms; determining, by the device, summarystatistics data for a set of junctions within the first geographicregion, the summary statistics data including information provided byone or more location aware devices associated with a set of vehiclestraveling through the set of junctions; training, by the device, a datamodel using the base map data and the summary statistics data, wheretraining the data model comprises: associating the base map data and thesummary statistics data from a junction of the set of junctions with oneor more training values, the one or more training values including alikelihood of a traffic control mechanism being located at the junction, the traffic control mechanism including a traffic light, a stop sign,or a yield sign; and classifying, by the device, the junction as havinga particular traffic control mechanism based on associating the base mapdata and the summary statistics data with the one or more trainingvalues; obtaining, by the device and after training the data model,additional base map data associated with a second geographic region;determining, by the device, additional summary statistics data for a setof junctions within the second geographic region; determining, by thedevice, traffic control mechanisms associated with the set of junctionswithin the second geographic region by providing the additional base mapdata and the additional summary statistics data as input for the datamodel; generating, by the device, a base map that includes informationindicating whether the set of junctions included within the secondgeographic region include traffic control mechanisms; and performing, bythe device, one or more actions associated with improving vehiclenavigation or traffic management.
 15. The method of claim 14, wheredetermining the summary statistics data comprises: obtaining observationdata for the set of vehicles traveling through the set of junctionswithin the first geographic region, the observation data identifying oneor more locations of the set of vehicles at one or more time periods,determining path data for the set of vehicles by analyzing theobservation data, the path data including information indicating vehiclespeed and information indicating vehicle direction, and determining thesummary statistics data by analyzing the path data for the set ofvehicles.
 16. The method of claim 14, where determining the summarystatistics data comprises: determining summary statistics data for oneor more vehicles of the set of vehicles, the one or more vehicles beingassociated with vehicles traveling through the set of junctions withinthe first geographic region in a straight line path.
 17. The method ofclaim 14, where training the data model comprises: assigning weights toone or more values included in the base map data and one or more valuesincluded in the summary statistics data, and where classifying thejunction as having the particular traffic control mechanism comprises:classifying the junction based on associating the one or more valuesincluded in the base map data and the one or more values included in thesummary statistics data with the one or more training values and basedon assigning weights to the one or more values included in the base mapdata and the summary statistics data.
 18. The method of claim 14, wheredetermining the traffic control mechanisms comprises: determining thetraffic control mechanisms by providing the additional base map data andthe additional summary statistics data as input for the data model, thedata model to output a set of classifications for the set of junctionswithin the second geographic region, the set of classificationsindicating whether the set of junctions within the second geographicregion include traffic control mechanisms; and where generating the basemap comprises: generate the base map that includes the informationindicating whether the set of junctions included within the secondgeographic region include traffic control mechanisms, the informationindicating whether the set of junctions within the second geographicregion include traffic control mechanisms being based on the set ofclassifications indicating whether the set of junctions within thesecond geographic region include traffic control mechanisms.
 19. Themethod of claim 14, where performing the one or more actions comprises:analyzing metadata for the set of junctions to determine whethermetadata for one or more junctions of the set of junctions satisfy athreshold, generating a recommendation to install, remove, or make amodification to a traffic control mechanism based on determining whetherthe one or more junctions satisfy the threshold.
 20. The method of claim14, further comprising: obtaining test information associated with a setof junctions with known traffic control mechanisms; providing the testinformation as input for the data model; determining whether output ofthe data model satisfies a threshold level of accuracy; and retrainingthe data model based on whether the output of the data model satisfiesthe threshold level of accuracy.